System management method, management computer, and non-transitory computer-readable storage medium

ABSTRACT

A system management method for a management computer coupled to a computer system, the computer system including a plurality of computers, an operations system being built thereon the computer system, the operations system including a plurality of task nodes each having allocated thereto computer resources, the system management method including: a step of analyzing a configuration of the computer system for specifying a important node, which is an important task node in the operations system; a step of changing an allocation amount of the computer resources allocated to the important node for measuring a load of the operations system; a step of calculating a first weighting representing a strength of associations among the plurality of task nodes based on a measurement result of the load; and a step of specifying a range impacted by a change in the load of the important node based on the calculated first weighting.

BACKGROUND OF THE INVENTION

This invention relates to a method of managing an operations systembuilt on a computer system including a plurality of computers.

Along with the developments in cloud computing in recent years, thenumber of data centers operating computer resources as a resource poolis increasing. A provider of the data center builds an operations systemfor a user in the data center by allocating predetermined computerresources from the resource pool based on a user request. The userprovides a predetermined service, such as a Web service, by using theoperations system built in the data center.

In order to operate the resource pool, it is important for the providerto grasp the performance limits of the systems in the data center, andexpand the scale of the data center as necessary. In general, the scaleof the data center is expanded by increasing the computer resources ofthe data center by scaling out.

A method of managing a computer system is described in JP 2008-225995 A,for example. In JP 2008-225995 A, there is described “a policy creationsupport method for a policy creation support system used in order tocontrol a system to be monitored so as to satisfy a contract condition,the policy creation support system being configured to support creationof a policy that contains a condition indicating a state of the systemto be monitored and an action to be executed when the state of thesystem to be monitored satisfies the condition. The condition contains amonitoring item serving as an item to be monitored in the system to bemonitored and a range of a measurement value of the monitoring item. Thepolicy creation support system is configured to execute a holding stepof holding a template for designating a type of the monitoring itemrequired for the creation of the policy, an obtaining step of obtainingthe measurement value of each resource amount of a resource to beexpanded for each monitoring item corresponding to the type designatedby the template, a selecting step of selecting one representativemeasurement value from among the measurement values for each resourceamount for each monitoring item corresponding to the type designated bythe template, and an output step of outputting the monitoring item, theresource amount of the monitoring item, and the range of the measurementvalue corresponding to the resource amount, for each monitoring itemcorresponding to the type designated by the template by setting a rangeincluding the selected representative measurement value as the range ofthe measurement value.”

However, the computer system and the operations system are independentof each other. Further, the configuration of the operations system isdifferent for each user. Therefore, the performance limits of thecomputer system and operations system cannot be easily grasped. As aresult, it is difficult to estimate the limits of scaling out, anddifficult to estimate the units for increasing the computer resources.In addition, what kind of expansion to perform depends on theconfiguration of the computer system and operations system.

Further, the method used to deal with system changes is different foreach data center depending on the situation, such as when theperformance of the computer resources of the data center is not uniform,when clusters are formed in the data center, when the data center isbuilt only from low-cost servers, or the like. The configuration of theoperations system also suffers from the same problem. Therefore, for adata center unable to handle system changes simply by scaling out, it isdifficult to estimate the performance limits of the computer system andoperations system.

SUMMARY OF THE INVENTION

It is an object of this invention to estimate performance limits of acomputer system and an operations system by performing an effectivestress test based on the associations of each of a plurality of nodesconstructing the operations system.

The present invention can be appreciated by the description whichfollows in conjunction with the following figures, wherein: a systemmanagement method for a management computer coupled to a computersystem, the management computer including: a first processor; a firstmemory coupled to the first processor; and a first interface coupled tothe first processor. The computer system includes a plurality ofcomputers, each of the plurality of computers includes: a secondprocessor; a second memory coupled to the second processor; and a secondinterface coupled to the second processor. An operations system is builton the computer system. The operations system includes a plurality oftask nodes each having allocated thereto one of computer resources ofone computer among the plurality of computers and computer resources ofa virtual computer generated on at least one computer among theplurality of computers. The system management method includes: a firststep of analyzing, by the management computer, a configuration of thecomputer system for specifying at least one important node, which is animportant task node in the operations system; a second step of changing,by the management computer, an allocation amount of the computerresources allocated to the at least one important node for measuring aload of the operations system; a third step of calculating, by themanagement computer, a first weighting representing a strength ofassociations among the plurality of task nodes based on a measurementresult of the load; and a fourth step of specifying, by the managementcomputer, a range impacted by a change in the load of the at least oneimportant node based on the calculated first weighting.

According to one embodiment of this invention, the load on theoperations system may be measured by focusing on important nodesspecified based on the configuration of the computer system from whichthe operations system is built. Further, the impact range of theimportant nodes may be specified based on the strength of theassociations among the task nodes. As a result, the performance limit ofthe operations system may be estimated.

Objects, configurations, and effects other than those described abovebecome apparent from the following descriptions of embodiments of thisinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention can be appreciated by the description whichfollows in conjunction with the following figures, wherein:

FIG. 1 is a block diagram for illustrating an outline of an embodimentof this invention;

FIG. 2 is an explanatory diagram for illustrating an example of aconfiguration of a overall system according to a first embodiment ofthis invention;

FIG. 3 is a block diagram for illustrating a configuration example of amanagement server of the first embodiment;

FIG. 4 is a block diagram for illustrating a configuration example ofblade servers of the first embodiment;

FIG. 5 is an explanatory diagram for illustrating an example of atopology management table of the first embodiment;

FIG. 6 is an explanatory diagram for illustrating an example of alogical configuration management table of the first embodiment;

FIG. 7 is an explanatory diagram for illustrating an example of a taskmanagement table of the first embodiment;

FIG. 8 is an explanatory diagram for illustrating an example of a nodemanagement table of the first embodiment;

FIG. 9 is an explanatory diagram for illustrating an example of athreshold management table of the first embodiment;

FIG. 10 is an explanatory diagram for illustrating an example of anadjustment method management table of the first embodiment;

FIG. 11 is an explanatory diagram for illustrating an example of asystem performance table of the first embodiment;

FIG. 12 is an explanatory diagram for showing an example of a weightingmanagement table of the first embodiment;

FIG. 13 is an explanatory diagram for showing an example of a chart ofthe weighting management table of the first embodiment in matrix form;

FIG. 14 is an explanatory diagram for illustrating an example of a rulemanagement table of the first embodiment;

FIG. 15 is a flowchart for illustrating an outline of processingexecuted by the management server of the first embodiment;

FIG. 16 is a flowchart for illustrating an example of node selectionprocessing of the first embodiment;

FIG. 17 is a flowchart for illustrating an example of measurementprocessing of the first embodiment;

FIG. 18 is an explanatory diagram for illustrating an example of ameasurement result of the first embodiment;

FIG. 19 is a flowchart for illustrating an example of countingprocessing of the first embodiment;

FIG. 20 is a flowchart for illustrating an example of monitoringprocessing of the first embodiment;

FIG. 21 is an explanatory diagram for illustrating an example of anorientation parameter registration screen of a second embodiment;

FIG. 22 is a flowchart for illustrating an example of processing forgenerating a superposition matrix of a fourth embodiment;

FIG. 23 is a block diagram for illustrating a configuration example ofthe management server of a fifth embodiment; and

FIG. 24 is a flowchart for illustrating an example of meta taggeneration processing of the fifth embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 is a block diagram for illustrating an outline of an embodimentof this invention.

In FIG. 1, an operations system configured to execute predeterminedtasks and a management server 100 configured to manage the operationssystem are coupled to each other via a management network. Further, theoperations system is coupled to an external network via a task network.

The operations system illustrated in FIG. 1 includes one database server10, two application servers 11, four Web servers 12, and one loadbalancer 13. In the following description, the elements, such as thedatabase server, constructing the operations system are also referred toas task nodes.

The operations system is built on a computer system providing computerresources. The computer system includes a physical layer built fromphysical computers, physical switches, and the like, and a logical layerbuilt from virtual computers realized using physical computers, virtualswitches realized using physical switches, and the like. In thefollowing description, the physical computers and the like constructingthe physical layer are also referred to as physical nodes, and thevirtual computers and the like constructing the logical layer are alsoreferred to as logical nodes.

The task nodes are realized using the physical nodes and the logicalnodes. For example, in realizing the database server 10, the databaseserver may be realized using a blade server 223, which is a physicalnode, or the database server may also be realized using a virtualcomputer, namely, a logical node, created on the blade server 223.

This embodiment is directed to estimating a performance limit of theoperations system. In order to estimate the performance limit, tasknodes having strict performance requirements in the operations systemare specified, and a stress test is carried out on the specified tasknodes based on a sequence such as that described below.

(1) First, the management server 100 specifies important task nodes inthe operations system based on the configuration of the operationssystem, and the configuration of the computer system from which theoperations system is built. In the following description, the importanttask nodes are also referred to as important nodes.(2) The management server 100 carries out a stress test on the importantnodes, and measures the impact on the overall operations system.(3) Based on the measurement result of the stress test, the managementserver 100 calculates a minimum cut set (impact range), which is acombination of nodes having a large impact on the operations system. Itshould be noted that the minimum cut set includes one or more importantnodes. Further, the management server 100 estimates the size of theimpact on the task nodes included in the impact range caused by changesin the load of the important nodes.

Based on the processing such as that described above, the performancelimit (performance characteristic) of the operations system can beestimated.

In the example illustrated in FIG. 1, the measurement result of a stresstest carried out when the amount of allocated resources of the databaseserver specified as an important node has been reduced by 30% is shown.In this case, a minimum cut set such as that indicated by the dottedline is calculated. In other words, in the operations system, the tasknodes that are impacted by the load of the database server, which is animportant node, can be specified.

Further, in this embodiment, the management server 100 is configured toexecute monitoring processing based on the measurement result of astress test carried out during operation of the operations system. Inthis case, the management server 100 displays a meter 15 representing anestimated value of the load of the task nodes included in the impactrange, a critical value thereof, or the like. As a result, an estimationcan be made regarding whether or not expansion of the computer systemand the operations system is necessary.

As a result, an alert can be issued to the provider or to the userbefore the load of the operations system reaches a certain level ormore, which allows countermeasures to be drawn up in advance.

First Embodiment

FIG. 2 is an explanatory diagram for illustrating an example of aconfiguration of the overall system according to a first embodiment ofthis invention.

The system according to the first embodiment includes a managementserver 100 and a computer system from which the operations system isbuilt. The computer system, which is a system providing computerresources, includes a plurality of server apparatus 220, a storagesubsystem 260, an NW-SW 250, and an FC-SW 140.

The management server 100 is configured to manage the computer system.The management server 100 is coupled to a management interface(management I/F) 211 of an NW-SW 210 (management network switch) 210 anda management interface 251 of an NW-SW (task network switch) 250 via theNW-SW 210. The management server 100 can set a virtual local areanetwork (VLAN) to both the NW-SW 210 and the NW-SW 250.

The NW-SW 210 forms a management network. The management network is anetwork that allows the management server 100 to distribute an operatingsystem (OS) and applications running on the plurality of serverapparatus 220, and to operate and manage power control and the like.

The NW-SW 250 forms a task network. The task network is a network usedby the applications to be executed by the server apparatus 220 or avirtual machine (VM) on the server apparatus 220. It should be notedthat the NW-SW 250 is configured to communicate with an external clientcomputer coupled via a wide area network (WAN) or the like.

In the following description, physical computer resources, such as theserver apparatus 220, the FC-SW 140, the NW-SW 250, and the storagesubsystem 260, are referred to as physical nodes, and virtual computerresources, such as the VM and the virtual switches, are referred to aslogical nodes.

The management server 100 is coupled to a management interface(management I/F) 161 of the storage subsystem 260 via the fiber channelswitch (FC-SW) 140. The management server 100 is configured to managelogical units (LUs) 262 in the storage subsystem 260. In the exampleillustrated in FIG. 1, the management server 100 manages N-number oflogical units LU 1 to LU-n.

The management server 100, which includes a control module 201, stores amanagement table group 202. The management table group 202 containsinformation on the configuration of the computer system, for example.The control module 201 is configured to execute a stress test (black boxtest) on the operations system built on the computer system by referringto the information contained in the management table group 202, andupdate the information contained in the management table group 202 basedon the measurement result of the stress test. Details of theconfiguration of the management server 100 are described later withreference to FIG. 3.

Each server apparatus 220 provides computer resources to be allocated tothe task nodes in a manner described later. The server apparatus 220 arecoupled to the NW-SW 210 and the NW-SW 250 via an input/output (I/O)device or the like.

Each server apparatus 220 has a service processor 221 and a plurality ofblade servers 223 mounted thereon. The service processor 221 isconfigured to monitor the blade servers 223 mounted on the serverapparatus 220. Each server apparatus 220 is coupled to the managementserver 100 via the service processor 221. The blade servers 223 have anOS and applications running thereon. The software configuration and thehardware configuration of the blade servers 223 are described later withreference to FIG. 4.

It should be noted that one or more VMs may be created on the bladeservers 223, and one or more virtual switches may be created on theFC-SW and the NW-SW 250. A virtual switch may also be created on theblade servers 223.

It should be noted that although the server apparatus 220 includes ablade server 223, this embodiment is not limited to this. For example, acommon computer including a processor, a memory, a network interface,and the like may be used as the server apparatus 220.

The storage subsystem 260 provides a storage area to be used by the OSand the like running on the server apparatus 220. The storage subsystem260 includes a controller (not shown), a plurality of storage media (notshown), a disk interface (not shown), and a network interface (notshown). Examples of the storage media include a hard disk drive (HDD)and a solid state drive (SSD).

The controller (not shown), which forms a redundant array of independentdisks (RAID) system using a plurality of storage media, is configured tocreate a plurality of LUs 162 from RAID volumes. The storage subsystem260 provides the LUs 162 as storage areas to be used by the OS and thelike.

FIG. 3 is a block diagram for illustrating a configuration example ofthe management server 100 of the first embodiment.

The management server 100 includes a processor 301, a memory 302, a diskinterface 303, and a network interface 304.

The processor 301 is configured to execute programs stored in the memory302. The memory 302 stores programs to be executed by the processor 301and information required to execute those programs. The programs andinformation stored in the memory 302 are described later.

The disk interface 303 is an interface for accessing the storagesubsystem 260. The network interface 304 is an interface forcommunicating to/from another apparatus via an Internet protocol (IP)network.

It should be noted that, although not shown in FIG. 3, the managementserver 100 may also include a basement management controller (BMC)configured to control the power supply and each of the interfaces.

A program for realizing the control module 201 and the management tablegroup 202 are stored in the memory 302. The control module 201 includesa plurality of program modules. Specifically, the control module 201includes a weighting calculation module 310, a measurement module 311,an estimation module 312, and an operations system monitoring module313.

The processor 301 is configured to operate as a function module forrealizing a predetermined function by operating based on program modulesfor realizing the weighting calculation module 310, the measurementmodule 311, the estimation module 312, and the operations systemmonitoring module 313. For example, by operating based on the programmodule for realizing the weighting calculation module 310, the processor301 functions as the weighting calculation module 310. This is the samefor the other programs as well.

The weighting calculation module 310 is configured to calculate aweighting for evaluating an importance level of each of a plurality oftask nodes in the operations system. The weighting calculation module310 of the first embodiment calculates the weighting of the task nodesbased on the configuration of the physical computer resources (physicalnodes) and the configuration of the logical computer resources (logicalnodes). Details of the processing executed by the weighting calculationmodule 310 are described later with reference to FIG. 16.

The measurement module 311 is configured to measure the impact on theoperations system by executing the predetermined stress test (black boxtest) on the task nodes. Details of the processing executed by themeasurement module 311 are described later with reference to FIG. 17.

The estimation module 312 is configured to generate information forestimating the performance limit of the operations system based on ameasurement result. Details of the processing executed by the estimationmodule 312 are described later with reference to FIG. 19.

The operations system monitoring module 313 is configured to monitor theoperations system based on the information on the performance limit ofthe operations system, and display a monitoring result. Further, theoperations system monitoring module 313 may also display processingresults of the weighting calculation module 310, the measurement module311, and the estimation module 312. Details of the processing executedby the operations system monitoring module 313 are described later withreference to FIG. 20.

The management table group 202 stores various kinds of information formanaging the computer system and the operations system. Specifically,the management table group 202 stores a topology management table 320, alogical configuration management table 321, a task management table 322,a node management table 323, a threshold management table 324, anadjustment method management table 325, a system performance table 326,a weighting management table 327, and a rule management table 328.

It should be noted that there is one node management table 323, onesystem performance table 326, and one weighting management table 327 foreach operations system.

The topology management table 320 stores information on the physicalnodes. Details of the topology management table 320 are described laterwith reference to FIG. 5. The logical configuration management table 321stores information on the logical nodes. Details of the logicalconfiguration management table 321 are described later with reference toFIG. 6. The task management table 322 stores information on the programsto be executed by the operations system. Details of the task managementtable 322 are described later with reference to FIG. 7. The nodemanagement table 323 stores information on the task nodes. Details ofthe node management table 323 are described later with reference to FIG.8.

The threshold management table 324 stores a threshold to be used forselecting the important nodes based on the weighting of the task nodes.Details of the threshold management table 324 are described later withreference to FIG. 9. The adjustment method management table 325 storesstress test policies. Details of the adjustment method management table325 are described later with reference to FIG. 10.

The system performance table 326 stores stress test measurement results.Details of the system performance table 326 are described later withreference to FIG. 11. The weighting management table 327 storesinformation on the importance level of the task nodes and associationsamong the task nodes in the operations system. Details of the weightingmanagement table 327 are described later with reference to FIG. 12.

The rule management table 328 stores methods of changing theconfiguration of the operations system or computer system, and thechange content. Details of the rule management table 328 are describedlater with reference to FIG. 14.

The programs for realizing the control module 201 and each of the tablesin the management table group 202 may be stored in the storage subsystem260, a storage device such as a non-volatile semiconductor memory, ahard disk drive, or a SSD, or a non-transitory computer-readable datastorage medium such as an integrated circuit (IC) card, a secure digital(SD) card, or a digital versatile disc (DVD).

It should be noted that the type of server for the management server 100may be any of a physical server, a blade server, a virtual server, alogically-partitioned or physically-partitioned server, and the like.The effects of this embodiment can be obtained regardless of the type ofserver that is used.

FIG. 4 is a block diagram for illustrating a configuration example ofthe blade servers 223 of the first embodiment.

Each blade server 223 includes a processor 401, a memory 402, a networkinterface 403, a disk interface 404, a BMC 405, and a peripheralcomponent interconnect (PCI) Express interface 406.

The processor 401 is configured to execute programs stored in the memory402. The memory 402 stores programs to be executed by the processor 401and information required to execute those programs. The programs andinformation stored in the memory 302 are described later.

The network interface 403 is an interface for communicating to/fromanother apparatus via an IP network. The disk interface 404 is aninterface for accessing the storage subsystem 260.

The BMC 405 controls the power supply and each of the interfaces. ThePCI-Express interface 406 is an interface for coupling to a PCIex-SW.

An OS 411 and programs for realizing an application 421 and a monitoringmodule 422 are stored in the memory 402. The processor 401 manages thedevices in the blade server 223 by executing the OS 411 on the memory402. The application 421 providing the task and the monitoring module422 operate under the control of the OS 411.

It should be noted that, as described below, the memory 402 may alsostore a program for realizing a virtualization module configured tomanage a virtual computer.

It should also be noted that in the example illustrated in FIG. 4, onenetwork interface 403, one disk interface 404, and one PCI-Expressinterface 406 are shown. However, a plurality of each of thoseinterfaces may be arranged. For example, each blade server 223 mayinclude a network interface coupling to the NW-SW 210 and a networkinterface coupling to the NW-SW 250.

FIG. 5 is an explanatory diagram for illustrating an example of thetopology management table 320 of the first embodiment.

The topology management table 320 stores information on the physicalconfiguration of the computer system from which the operations system isbuilt. Specifically, the topology management table 320 includes anidentifier 501, a UUID 502, a physical node identifier 503, a devicename 504, a property 505, a coupling destination device name 506, areliability type 507, and a eigenvalue 508.

The identifier 501 is an identifier for uniquely identifying entries inthe topology management table 320. The UUID 502 stores a universalunique identifier (UUID), which is an identifier having a format that isdefined so as not to be duplicated. The physical node identifier 503 isan identifier for uniquely identifying the physical nodes constructingthe physical layer. In this embodiment, an apparatus identifier is usedas the identifier of the physical nodes.

The device name 504 is an identifier for uniquely identifying thedevices the physical nodes have. In the case of an entry representing aphysical node itself, the device name 504 is left blank.

The property 505 is information representing the performance of aphysical node itself, or, information representing the performance ofthe device corresponding to the device name 504. When a port name of theFC-SW 240 is stored in the device name 504, information on a vendertype, a RAID configuration, a virtualization type, a support function, afirmware version, and the like is stored in the property 505.

The coupling destination device name 506 stores information on anotherdevice coupled to the device corresponding to the device name 504. Itshould be noted that when the device name 504 is blank, that is, for anentry corresponding to a physical node itself, the identifier of theanother physical node connected to the physical node is stored in thecoupling destination device name 506. The reliability type 507 isinformation on the configuration, such as redundancy, of the physicalnode or of the device corresponding to the coupling destination devicename 506.

The eigen value 508 is a value for evaluating the importance level ofthe physical node or device corresponding to the entry. In other words,the eigen value 508 is a value for evaluating the importance level ofthe configuration of the physical layer in the computer system. In thiscase, the configuration of the physical layer includes the hardwareconfiguration and software configuration of the physical nodes, and theconnection configuration among the plurality of physical nodes. Theeigen value 508 may be set in advance, or may be set by theadministrator or the like operating the management server 100.

FIG. 6 is an explanatory diagram for illustrating an example of thelogical configuration management table 321 of the first embodiment.

The logical configuration management table 321 stores information oncharacteristics of the nodes in the operations system. Specifically, thelogical configuration management table 321 includes an identifier 601, aUUID 602, a logical node identifier 603, a type 604, an adjustmentmetric 605, an adjustment parameter 606, a physical node identifier 607,a system configuration 608, a cascade connection 609, and a eigen value610.

The identifier 601 is an identifier for uniquely identifying entries inthe logical configuration management table 321. The logical nodeidentifier 603 is an identifier for uniquely identifying the logicalnodes constructing the logical layer. In this embodiment, the identifierof the VM itself and the identifier of the virtual switch itself areused as identifiers of the logical nodes. The type 604 is informationrepresenting the type of performance changing method available to thelogical nodes.

The adjustment metric 605 is an item measured in order to grasp the loadof the logical nodes in the black box test. For example, “CPU”, “I/O”,and “memory” are stored in the adjustment metric 605 for the entryindicated by “1” in the identifier 601, and hence processor usage, I/Ocount, memory usage, or the like of logical node 1 is measured. Theadjustment parameter 606 is a parameter for performing an adjustmentwhen changing the load on the logical nodes in the black box test.

The physical node identifier 607 is an identifier of a physical nodeallocating computer resources to a logical node. The physical nodeidentifier 607 is the same as the physical node identifier 503.

The system configuration 608 is information on the configuration of thelogical nodes in the logical layer. In the example shown in FIG. 6,“scale out type”, “scale up type”, and “mesh type” are stored in thesystem configuration 608 as information representing the configurationof the system. For example, “scale out type” represents a configurationin which logical nodes of the same type perform processing in parallel.Further, a eigen value for the configuration of the logical nodes in thelogical layer is set in the system configuration 608. For example, “1.0”is set as the eigen value in the system configuration 608 for the entryindicated by “1” in the identifier 601. In addition, the systemconfiguration 608 stores an identifier of the logical nodes forming acluster, or, an identifier of logical nodes connected in parallel, forexample.

The cascade connection 609 is an identifier of a logical node connectedto a logical node corresponding to the logical node identifier 603 in aseries configuration. An example of the series configuration is a Webthree-tier model. The eigen value 610 is a value for evaluating theimportance level of the logical node corresponding to the logical nodeidentifier 603. In other words, the eigen value 610 is a value forevaluating the importance level of the configuration of the logicallayer in the computer system. In this case, the configuration of thelogical layer includes the hardware configuration and softwareconfiguration of the logical nodes, and the connection configurationamong the plurality of logical nodes. The eigen value 610 may be set inadvance, or may be set by the administrator or the like operating themanagement server 100.

FIG. 7 is an explanatory diagram for illustrating an example of the taskmanagement table 322 of the first embodiment.

The task management table 322 stores information on the tasks to beexecuted by the operations system. Specifically, the task managementtable 322 stores a task identifier 701, a UUID 702, a task software name703, task setting information 704, a priority order 705, and a eigenvalue 706.

The task identifier 701 is an identifier for uniquely identifying thetasks in the operations system. The task software name 703 is a name ofthe software to be executed in order to provide a task. The task settinginformation 704 is setting information required in order to execute thetask corresponding to the task identifier 701. The priority order 705 isa priority order of the tasks in the operations system. A smaller valuefor the priority order 705 indicates a more important task. Further, thepriority order 705 also stores information on the configuration requiredfor the task. The eigen value 706 is a value for evaluating theimportance level of the task.

FIG. 8 is an explanatory diagram for illustrating an example of the nodemanagement table 323 of the first embodiment.

The node management table 323 stores information on the task nodesconstructing the operations system. Specifically, the node managementtable 323 stores a task node identifier 801, a UUID 802, an assignednode identifier 803, a task type 804, a task identifier 805, a connectednode identifier 806, and associated information 807.

The task node identifier 801 is an identifier for uniquely identifyingtask nodes in the operations system.

The assigned node identifier 803 is an identifier of a node providingcomputer resources to a task node. When a physical node providescomputer resources to a task node, the identifier of the physical nodeis stored in the assigned node identifier 803. When a logical nodeprovides computer resources to a task node, the identifier of thelogical node is stored in the assigned node identifier 803.

The task type 804 is the type of task to be executed by the task node.The task identifier 805 is an identifier of the task corresponding tothe task type 804. The task identifier 805 is the same as the taskidentifier 701. The connected node identifier 806 is an identifier ofanother task node connected to the task node corresponding to the tasknode identifier 801. The associated information 807 is information onthe configuration required for the task node.

As shown in FIG. 8, a eigen value is not set for the task nodes. Thereason for this is because even for identical operations systems, if theconfiguration of the computer systems from which those operationssystems are built is different, or if the content of the tasks to beexecuted by the operations systems is different, the associations amongthe task nodes and the performance limits of the task nodes are alsodifferent.

Therefore, in this embodiment, the management server 100 is configuredto calculate a weighting representing an importance level of the tasknodes based on the configuration of the physical layer, theconfiguration of the logical layer, and the content of the tasks. Themanagement server 100 is configured to specify the important nodes ofthe operations system based on the calculated weighting of the tasknodes.

The important nodes have a high likelihood of having a large impact onthe operations system. Therefore, measurement can be efficiently carriedout by executing a stress test on the important nodes. Further, theperformance limit of the operations system can also be estimated bygrasping the behavior of the overall operations system with respect tothe load of the important nodes. It should be noted that the stress testmay be executed on task nodes other than the important nodes. Forexample, the stress test may be carried out on the task nodes connectedto the important nodes.

This embodiment improves calculation efficiency and increases stresstest accuracy by executing the stress test only on the important nodes.

In this embodiment, there is one node management table 323 for eachoperations system. An identifier of the operations system is associatedwith the node management table 323.

FIG. 9 is an explanatory diagram for illustrating an example of thethreshold management table 324 of the first embodiment.

The threshold management table 324 stores a threshold to be used whenthe management server 100 specifies the important nodes. Specifically,the threshold management table 324 includes an identifier 901, a taskidentifier 902, and a threshold 903.

The identifier 901 is an identifier for uniquely identifying entries inthe threshold management table 324. The task identifier 902 is anidentifier for uniquely identifying tasks in the operations system. Thetask identifier 902 is the same as the task identifier 701. Thethreshold 903 is a threshold for a task.

In this embodiment, a threshold is set for each task. However, a singlethreshold may be set for the whole operations system.

FIG. 10 is an explanatory diagram for illustrating an example of theadjustment method management table 325 of the first embodiment.

The adjustment method management table 325 stores information on amethod of adjusting the load on the physical nodes or the logical nodesin the stress test. Specifically, the adjustment method management table325 includes an identifier 1001, a node type 1002, and an adjustmentmethod 1003.

The identifier 1001 is an identifier for uniquely identifying entries inthe adjustment method management table 325. The node type 1002 is thetype of the node to be adjusted. In the node type 1002, “physicalserver”, “SW”, “VM”, “vSW”, and the like are stored.

The adjustment method 1003 includes information on the method ofadjusting the load on the physical nodes or the logical nodescorresponding to the node type 1002. Specifically, the adjustment method1003 includes an adjustment parameter type 1004, an adjustment value1005, and a priority 1006.

The adjustment parameter type 1004 is the type of parameter (adjustmentparameter) to be adjusted at the node corresponding to the node type1002. The adjustment value 1005 is an adjustment value of the adjustmentparameter. The priority 1006 is a priority order of the adjustmentmethod. More specifically, in the priority 1006, a value is stored fordetermining the adjustment method to be employed when there are aplurality of entries having the same node type 1002 and adjustmentparameter type 1004, but a different adjustment value 1005. In thisembodiment, a smaller value indicates a higher priority order.

The content of the adjustment method is now described.

The entry indicated by “1” in the identifier 1001 has “VM” for the nodetype 1002, “CPU” for the adjustment parameter type 1004, and “−10%” forthe adjustment value 1005. In this case, the management server 100decreases the allocation ratio of the virtual processor allocated to theVM by “10%”, and measures the impact on the overall operations system.The management server 100 subsequently decreases the allocation ratio ofthe virtual processor by “10%” step by step, and measures the impact onthe overall operations system.

Further, the entry indicated by “5” in the identifier 1001 has “VM” forthe node type 1002, “VM” for the adjustment parameter type 1004, and“−1” for the adjustment value 1005. In this case, the management server100 decreases the number of VMs by “1”, and measures the impact on theoverall operations system. The management server 100 subsequentlydecreases the number of VMs by “1” step by step, and measures the impacton the overall operations system.

In addition, the entry indicated by “6” in the identifier 1001 has “VM”for the node type 1002, “CPU” for the adjustment parameter type 1004,and “vCPU−1 with reboot” for the adjustment value 1005. In this case,the management server 100 decreases the number of virtual processors forthe VM by “1”, reboots the system, and then measures the impact on theoverall operations system. The management server 100 subsequentlydecreases the number of virtual processors by “1” step by step, rebootsthe system, and then measures the impact on the overall operationssystem.

It should be noted that when the load on the operations system changessuddenly, adjustment may be carried out in even more incremental steps.

FIG. 11 is an explanatory diagram for illustrating an example of thesystem performance table 326 of the first embodiment.

The system performance table 326 stores information on a performancecharacteristic function generated based on the measurement result of thestress test. Specifically, the system performance table 326 includes anidentifier 1101, an important node identifier 1102, an associated nodeidentifier 1103, and a performance characteristic function 1104.

The identifier 1101 is an identifier for uniquely identifying entries inthe system performance table 326. In the system performance table 326,there is one entry for each important node. The important nodeidentifier 1102 is an identifier of the task nodes that are importantnodes. The associated node identifier 1103 is an identifier of the tasknodes that are impacted by the important nodes. One or more associatednodes exist for each important node. In the following description, thetask nodes impacted by the important nodes are also referred to asassociated nodes.

The performance characteristic function 1104 stores information on aperformance characteristic function representing a relationship betweenthe important nodes and the associated nodes. Specifically, theperformance characteristic function 1104 includes an adjustmentparameter type 1105 and a function 1106.

The adjustment parameter type 1105 is an adjustment parameter of theimportant nodes, and corresponds to a variable of the performancecharacteristic function. The function 1106 stores the performancecharacteristic function. In this embodiment, the performancecharacteristic function is calculated as a change in the load of anassociated node based on the adjustment parameter. The load of theassociated node is the processor usage. It should be noted that memoryusage, network usage, response time, and the like may also be used asthe load of the associated node.

In this case, “X” stored in the function 1106 represents the value ofthe adjustment parameter corresponding to the adjustment parameter type1105, and “Y” represents the load of the associated node.

In this embodiment, there is one system performance table 326 for eachoperations system. An identifier of the operations system is associatedwith the system performance table 326.

FIG. 12 is an explanatory diagram for showing an example of theweighting management table 327 of the first embodiment. FIG. 13 is anexplanatory diagram for showing an example of a chart of the weightingmanagement table 327 of the first embodiment in matrix form.

The weighting management table 327 stores information on the strength ofthe associations among the task nodes, which is calculated based on themeasurement result of the stress test. Specifically, the weightingmanagement table 327 includes an identifier 1201, a type 1202, aparameter type 1203, a task node identifier 1204, and a weighting 1205.

The identifier 1201 is an identifier for uniquely identifying entries inthe weighting management table 327. The type 1202 is the type of objectfor which a weighting is to be calculated. In the case of an entrycorresponding to the weighting of a node, “node” is stored in the type1202. In the case of an entry corresponding to the weighting of an edgebetween two nodes, “edge” is stored in the type 1202. The parameter type1203 stores the type of parameter for which one task node has an impacton another task node.

The task node identifier 1204 is an identifier of a task node associatedwith the type 1202. For example, when “node” is stored in the type 1202,the identifier of one task node is stored in the task node identifier1204, and when “edge” is stored in the type 1202, the identifiers of thetwo task nodes connected by the edge are stored in the task nodeidentifier 1204.

The weighting 1205 is the weighting of a task node or an edge. In thiscase, the weighting of a task node is the value for evaluating theimportance level of the task node in the operations system. Theweighting of an edge is the value for evaluating the strength of theassociation between the task nodes.

The weighting management table 327 shown in FIG. 12 can be representedas data in matrix form, such as that shown in FIG. 13. In this case, thediagonal components of the matrix correspond to the weighting of thenodes, and the off-diagonal components correspond to the weighting ofthe edges. For example, matrix component A11 corresponds to theweighting of the task node 1, and matrix component A12 corresponds tothe weighting of the edge connecting the task node 1 and the task node2.

There is a possibility that some matrix element entries are not presentin the weighting management table 327. In this embodiment, when an entrycorresponding to a matrix element is not present, the value for thatmatrix element is taken to be “0”.

In this embodiment, there is one weighting management table 327 for eachoperations system. An identifier of the operations system is associatedwith the weighting management table 327.

FIG. 14 is an explanatory diagram for illustrating an example of therule management table 328 of the first embodiment.

The rule management table 328 stores a method of changing theconfiguration of the operations system or computer system, and thechange content. Specifically, the rule management table 328 includes atask identifier 1401, a UUID 1402, a task type 1403, associatedinformation 1404, a priority order 1405, and a rule 1406.

The task identifier 1401 is an identifier for uniquely identifying tasksin the operations system. The task identifier 1401 is the same as thetask identifier 701. The task type 1403 is the type of task to beexecuted by the task node. The task type 1403 is the same as the tasktype 804. The associated information 1404 is information on theconfiguration required for the task corresponding to the task type.

The priority order 1405 is a priority order of the tasks in theoperations system. In this embodiment, a smaller value for the priorityorder 1405 indicates a higher priority order. The rule 1406 is thechange content of the specific configuration of the operations system.In this embodiment, one or more pieces of change content are stored inthe rule 1406. In this case, all of the pieces of change content may beapplied, or the change content may be applied up to the point whenperformance of the operations system has improved.

FIG. 15 is a flowchart for illustrating an outline of processingexecuted by the management server 100 of the first embodiment.

The control module 201 of the management server 100 starts theprocessing when an instruction to execute a stress test is received fromthe user or the like. It should be noted that the trigger for themanagement server 100 to start the processing is not limited to this.For example, the management server 100 may execute the processingperiodically, or may start the processing when a change to theoperations system or computer system is detected. The identifier of theoperations system on which processing is to be executed is input to themanagement server 100.

First, the control module 201 executes node selection processing (StepS100). In the node selection processing, the control module 201 analyzesthe configuration of the computer system, and selects one or moreimportant nodes from among the plurality of task nodes constructing theoperations system based on the analysis result. Details of the nodeselection processing are described later with reference to FIG. 16.

The control module 201 then executes measurement processing (Step S101),and counting processing based on the result of the measurementprocessing (Step S102). In the measurement processing, a stress testfocused on the important nodes selected in the node selection processingis executed. In the counting processing, based on the result of themeasurement processing, information representing an association betweentwo task nodes is generated. Details of the measurement processing aredescribed later with reference to FIG. 17. Further, details of thecounting processing are described later with reference to FIG. 19.

The processing from Step S100 to Step S102 is executed before operationof the operations system. After operation of the operations system hasstarted, the control module 201 executes monitoring processing on theoperations system based on the results of the measurement processing andthe counting processing (Step S103). Details of the monitoringprocessing are described later with reference to FIG. 20.

FIG. 16 is a flowchart for illustrating an example of the node selectionprocessing of the first embodiment. The weighting calculation module 310included in the control module 201 executes the node selectionprocessing.

The weighting calculation module 310 starts loop processing of the tasknodes (Step S200). At this stage, the weighting calculation module 310selects a task node to be processed from among the task nodes includedin the operations system.

Specifically, the weighting calculation module 310 selects one entry byreferring to the node management table 323 corresponding to the inputidentifier of the operations system. In this embodiment, the weightingcalculation module 310 makes the selection in order of the entries fromthe top of the node management table 323. It should be noted that thepriority order may be set in advance based on the task type or taskcontent, and the weighting calculation module 310 may be configured toselect the entry based on that priority order.

The weighting calculation module 310 obtains the eigen values associatedwith the selected task node from the topology management table 320 andthe logical configuration management table 321 (Step S201).Specifically, processing such as the following is executed.

The weighting calculation module 310 refers to the assigned nodeidentifier 803 of the entry selected from the node management table 323,and specifies a node providing computer resources to the selected tasknode.

When the node providing computer resources to the task node is aphysical node, the weighting calculation module 310 refers to thetopology management table 320, and retrieves all of the entries having aphysical node identifier 503 that matches the assigned node identifier803. The weighting calculation module 310 obtains the value of the eigenvalue 508 of all of the retrieved entries.

When the node providing computer resources to the task node is a logicalnode, the weighting calculation module 310 refers to the logicalconfiguration management table 321, and retrieves the entries having alogical node identifier 603 that matches the assigned node identifier803. The weighting calculation module 310 obtains the value included inthe system configuration 608 and the value of the eigen value 610 of theretrieved entries.

In addition, the weighting calculation module 310 refers to the topologymanagement table 320, and retrieves all of the entries having a physicalnode identifier 503 that matches the physical node identifier 607 of theentries retrieved from the logical configuration management table 321.The weighting calculation module 310 obtains the value of the eigenvalue 508 of all of the retrieved entries.

It should be noted that in this embodiment, the physical nodes such asthe blade server 223 are handled as task nodes, but the physical nodessuch as the NW-SW 250 are not handled as task nodes. In the task layer,the physical nodes such as the NW-SW 250 are handled as edges. As aresult, when the weighting calculation module 310 calculates the eigenvalues, it is necessary to set in advance whether or not considerationneeds to be given to the physical nodes such as the NW-SW 250. In thisembodiment, the physical nodes that are taken into consideration whencalculating the eigen values for the task nodes are set in advance. Itshould be noted that the eigen values of the physical nodes included inthe edges may also be used as a weighting coefficient of the edges.

The above is a description of the processing carried out in Step S201.

Next, the weighting calculation module 310 calculates the weighting ofeach task node by using the eigen values obtained from the topologymanagement table 320 and the logical configuration management table 321(Step S202). Various methods of calculating the weighting of each tasknode may be employed. For example, a method such as the followingcalculation method may be employed.

When the node providing computer resources to the task node is aphysical node, the weighting calculation module 310 calculates a firsttotal value by adding together all of the values obtained from the eigenvalues 508 of the topology management table 320. This first total valueis taken as the weighting of the task node.

When the node providing computer resources to the task node is a logicalnode, the weighting calculation module 310 calculates a first totalvalue by adding together all of the values obtained from the eigen value508 of the topology management table 320. Further, the weightingcalculation module 310 calculates a second total value by addingtogether the values included in the system configuration 608 of thelogical configuration management table 321 and the values of the eigenvalues 610. In addition, the weighting calculation module 310 calculatesa third total value by adding together the first total value and thesecond total value. This third total value is taken as the weighting ofthe task node.

Further, a method using the second total value as the weightingcoefficient may be employed. In this case, the weighting calculationmodule 310 calculates the weighting of the task node by multiplying thefirst total value by the second total value.

It should be noted that the method of calculating the weighting of thetask node described above is one example. This embodiment is not limitedregarding the method of calculating the weighting of the task nodes thatis employed. Any method may be employed, as long as the method iscapable of calculating an index (weighting) for evaluating theimportance level of the task nodes based on the configuration of thephysical layer and the configuration of the logical layer.

Next, the weighting calculation module 310 updates the weightingmanagement table 327 based on the calculated weighting (Step S203).

Specifically, the weighting calculation module 310 adds an entry to theweighting management table 327, and sets a predetermined identifier inthe identifier 1201 of the added entry. The weighting calculation module310 sets “node” in the type 1202 of the added entry, and sets theidentifier of the task node to be processed in the task node identifier1204. Further, the weighting calculation module 310 sets the calculatedweighting in the weighting 1205. It should be noted that the parametertype 1203 remains blank.

The weighting calculation module 310 then determines whether or not theweighting of the task node is more than a threshold (Step S204).

Specifically, the weighting calculation module 310 refers to thethreshold management table 324, and retrieves an entry having a taskidentifier 902 that matches the task identifier 805 of the entryselected from the node management table 323. The weighting calculationmodule 310 then determines whether or not the weighting of the task nodeis more than the value of the threshold 903 of the retrieved entry.

When the weighting of the task node is determined to be equal to or lessthan the value of the threshold 903, the weighting calculation module310 proceeds the processing to Step S206.

When the weighting of the task node is determined to be more than thevalue of the threshold 903, the weighting calculation module 310registers the selected task node in a verification list (Step S205). Inthis case, the verification list is a list in which the important nodesare registered.

Specifically, the weighting calculation module 310 registers the entryof the selected task node in the verification list. In this embodiment,the weighting calculation module 310 registers an entry having the samecontent as the entry corresponding to the task node of the logicalconfiguration management table 321 in the verification list. Further,the weighting calculation module 310 sorts the entries stored in theverification list based on the weighting of the task node. In thisembodiment, the weighting calculation module 310 rearranges the entriesin descending order of the task node weighting.

It should be noted that when there is no verification list, theweighting calculation module 310 generates the verification list in awork area of the memory 302, and registers the entry for the task nodein the generated verification list.

The weighting calculation module 310 then determines whether or not theprocessing of all of the task nodes in the operations system to beprocessed is complete (Step S206). When it is determined that theprocessing of all of the task nodes in the operations system to beprocessed is not complete, the processing returns to Step S200, and theweighting calculation module 310 executes the same processing on a newtask node.

When it is determined that the processing of all of the task nodes inthe operations system to be processed is complete, the weightingcalculation module 310 finishes the processing.

FIG. 17 is a flowchart for illustrating an example of the measurementprocessing of the first embodiment. The measurement module 311 includedin the control module 201 executes the measurement processing.

The measurement module 311 starts loop processing of the important nodes(Step S300). At this stage, the measurement module 311 selects oneimportant node entry from the verification list. In this embodiment, theentries in the verification list are arranged in descending order of theweighting, and hence the measurement module 311 selects an entry inorder from the top of the verification list. Further, the measurementmodule 311 adds an entry to the system performance table 326, sets apredetermined identifier in the identifier 1101, and sets the identifierof the selected important node in the important node identifier 1102.

The measurement module 311 refers to the adjustment method managementtable 325, and specifies the verification method to be applied to theselected important node (Step S301). Specifically, processing such asthe following is executed.

The measurement module 311 obtains the identifier from the assigned nodeidentifier 803 of the entry of the selected important node.

(1) When the obtained identifier is an identifier of a physical node,the measurement module 311 specifies the apparatus based on the physicalnode identifier. When the apparatus is a blade server 223, themeasurement module 311 obtains all of the entries having “physicalserver” in the node type 1002 from the adjustment method managementtable 325. Further, when the apparatus is a switch, the measurementmodule 311 obtains all of the entries having “SW” in the node type 1002from the adjustment method management table 325.(2) When the obtained identifier is an identifier of a logical node, themeasurement module 311 specifies the logical apparatus based on thelogical node identifier. When the logical apparatus is a VM, themeasurement module 311 obtains all of the entries having “VM” in thenode type 1002 from the adjustment method management table 325. Further,when the logical apparatus is a virtual switch, the measurement module311 obtains all of the entries having “vSW” in the node type 1002 fromthe adjustment method management table 325.

In addition, the measurement module 311 refers to the topologymanagement table 320, specifies the physical node associated with thelogical node, and by using the same method as the method described in(1), obtains all of the entries corresponding to the physical node fromthe adjustment method management table 325.

The above is a description of the processing carried out in Step S301.

Next, the measurement module 311 starts loop processing of theverification method (from Step S302 to Step S309). In the loopprocessing of the verification method, the measurement module 311adjusts at least any one of the parameters of the physical node andlogical node for each specified verification method, and measures theload on the overall operations system.

First, the measurement module 311 selects one of the specifiedverification methods, and starts measurement processing based on theselected verification method (Step S302). At this stage, when there area plurality of entries having the same adjustment parameter type 1004and adjustment value 1005, the measurement module 311 selects an entrybased on the value of the priority 1006.

The measurement module 311 changes a parameter of the physical node orlogical node based on the selected verification method (Step S303), andmeasures various loads in the operations system (Step S304).

The measurement method employed in this embodiment is not limited. Forexample, an experimental design method may be employed. In thisembodiment, the load of each task node in the operations system ismeasured. For example, the measurement module 311 measures the processorusage, memory usage, network bandwidth usage, throughput, and the likeof the task node as the load. In this case, as an example, the number ofprocessed requests (throughput) at the task node is measured as theload.

Further, the measurement module 311 stores a measurement result 1800 ina work area of the memory 302, or in a storage area of the storagesubsystem 260. An example of the measurement result 1800 is nowdescribed with reference to FIG. 18.

FIG. 18 is an explanatory diagram for illustrating an example of themeasurement result 1800 of the first embodiment.

The measurement result 1800 shown in FIG. 18 has one entry for eachentry in the verification list. In other words, the measurement result1800 includes the same number of entries as important nodes. Each entryincludes an important node identifier 1801, an adjustment parameter type1802, a task node identifier 1803, a parameter value 1804, and ameasurement value 1805.

The important node identifier 1801 is an identifier of the importantnode selected in Step S300. The identifier stored in the important nodeidentifier 1801 is the same as the identifier stored in the task nodeidentifier 801. The adjustment parameter type 1802 is the type ofadjustment parameter adjusted in Step S303. The adjustment parametertype 1802 is the same as the adjustment parameter type 1004.

The task node identifier 1803 is an identifier of the task node on whichload measurement was carried out. The task node identifier 1803 is thesame as the task node identifier 801. The parameter value 1804 is avalue of the parameter actually adjusted based on the verificationmethod selected by the measurement module 311 in Step S302. Themeasurement value 1805 is a value representing the load of the task nodecorresponding to the task node identifier 1803 when the parameter hasbeen adjusted to the value indicated in the parameter value 1804.

The description now returns to FIG. 17.

The measurement module 311 determines whether or not processing of allof the specified verification methods is complete (Step S305). When itis determined that processing of all of the specified verificationmethods is not complete, the processing returns to Step S302, and themeasurement module 311 executes the same processing on a newverification method.

When it is determined that processing of all of the specifiedverification methods is complete, the measurement module 311 determineswhether or not processing of all of the important nodes is complete,that is, whether or not processing of all of the entries included in theverification list is complete (Step S306). When it is determined thatprocessing of all of the important nodes is not complete, the processingreturns to Step S300, and the measurement module 311 executes the sameprocessing on a new important node.

When it is determined that processing of all of the important nodes iscomplete, the measurement module 311 updates the system performancetable 326 based on the measurement result 1800 (Step S307), and thenfinishes the processing. Specifically, processing such as the followingis executed.

The measurement module 311 reads one entry from the measurement result1800, and adds one entry to the system performance table 326. Themeasurement module 311 sets a predetermined identifier in the identifier1101 of the added entry, and sets in the important node identifier 1102the identifier stored in the important node identifier 1801.

The measurement module 311 selects one adjustment parameter type 1802,and specifies a task node having a load that has changed by a fixedvalue or more. For example, the measurement module 311 calculates therate of change in the load by analyzing the measurement value 1805corresponding to the task node identifier 1803, and determines whetheror not the calculated rate of change in the load is 20% or more. A tasknode having a calculated rate of change in the load of 20% or more isspecified as a task node having a load that has changed by a fixed valueor more.

It should be noted that when a task node having an association with animportant node is known in advance based on the result of a previousstress test and the like, that is, when the system performance table 326used in a previous stress test is present in the memory 302, themeasurement module 311 may select that task node.

The measurement module 311 sets the identifier of the task nodespecified by the above-mentioned processing in the task node identifier1803 of the entry added to the system performance table 326. Further,the measurement module 311 sets information stored in the adjustmentparameter type 1802 selected in the above-mentioned processing in theadjustment parameter type 1105 of that entry.

The measurement module 311 calculates the performance characteristicfunction based on an analysis result of the above-mentioned processing.In this case, the measurement module 311 calculates the performancecharacteristic function based on the value of the adjustment parametercorresponding to the adjustment parameter type 1105 as the domain (X)and the throughput as the range (Y). The measurement module 311 sets thecalculated function in the function 1106. Then, the system performancetable 326 is updated based on the same procedure.

The above is a description of the processing carried out in Step S307.

FIG. 19 is a flowchart for illustrating an example of the countingprocessing of the first embodiment. The estimation module 312 includedin the control module 201 executes the counting processing.

First, the estimation module 312 starts loop processing of the importantnodes (Step S400). At this stage, the estimation module 312 selects oneentry from the system performance table 326. In the followingdescription, the selected entry is also referred to as an important nodeentry.

Next, the estimation module 312 starts loop processing of the adjustmentparameters (Step S401). Specifically, the estimation module 312 extractsall of the types of adjustment parameters from the adjustment parametertype 1105 of the important node entry, and selects one adjustmentparameter to be processed from among the extracted adjustmentparameters.

Next, the estimation module 312 starts loop processing of the task nodes(Step S402). Specifically, processing such as the following is executed.

The estimation module 312 extracts the associated node identifier fromthe task node identifier 1803 of the row including the selectedadjustment parameter in the adjustment parameter type 1105 of theimportant node entry. The estimation module 312 selects one associatednode identifier from among the extracted task node identifiers.

The estimation module 312 refers to the node management table 323 andspecifies another task node that is connected to the selected task node.The estimation module 312 calculates the number of task nodes passedthrough from the important node until the selected task node as a firsthop count. Further, the estimation module 312 calculates the number oftask nodes passed through from the important node until the specifiedtask node as a second hop count.

The estimation module 312 determines whether or not the first hop countis larger than the second hop count. When the first hop count is equalto or less than the second hop count, the estimation module 312 proceedsthe processing to Step S403. When the first hop count is larger than thesecond hop count, the estimation module 312 proceeds the processing toStep S405. The above is a description of the processing carried out inStep S402.

The estimation module 312 calculates the weighting of the edgeconnecting two task nodes (Step S403). Various methods may be employedas the method of calculating the weighting of the edge. For example, amethod such as the following may be used.

The estimation module 312 obtains the performance characteristicfunction from the function 1106 of the row matching the important node,associated node, and adjustment parameter selected in Step S400 to StepS402. The estimation module 312 calculates, as the weighting of theedge, a maximum value of the derivative of the performancecharacteristic function based on the performance characteristic functionand the domain of the variable X.

It should be noted that, depending on the type of performancecharacteristic function, there may be cases in which the derivativecannot be calculated. In that case, a method can be employed that uses,instead of the derivative, the average value, the total value, or thelike of the size of the load of the associated nodes resulting from achange in the adjustment parameter. In this case, the estimation module312 refers to the measurement result 1800, and obtains the requiredvalues from the measurement value 1805 matching the identifiers of theimportant node, the task node, and the adjustment parameter. The aboveis a description of the processing carried out in Step S403.

The estimation module 312 updates the weighting management table 327based on the calculated edge weighting (Step S404).

Specifically, the estimation module 312 adds an entry to the weightingmanagement table 327, and sets a predetermined identifier in theidentifier 1201 of the added entry. The estimation module 312 sets“edge” in the type 1202 of the added entry, and sets the selectedadjustment parameter in the parameter type 1203. Further, the estimationmodule 312 sets the identifiers of the selected task node and thespecified task node in the task node identifier 1204, and sets thecalculated edge weighting in the weighting 1205. It should be noted thatwhen a plurality of task nodes are connected to the selected task node,a plurality of entries are added to the weighting management table 327.

The estimation module 312 determines whether or not processing of all ofthe associated nodes is complete (Step S405). When it is determined thatprocessing of all of the associated nodes is not complete, theprocessing returns to Step S402, and the estimation module 312 executesthe same processing on a new associated node.

When it is determined that processing of all of the associated nodes iscomplete, the estimation module 312 determines whether or not processingof all of the adjustment parameters is complete (Step S406). When it isdetermined that processing of all of the adjustment parameters is notcomplete, the processing returns to Step S401, and the estimation module312 selects a new adjustment parameter and executes the same processing.

When it is determined that processing of all of the adjustmentparameters is complete, the estimation module 312 determines whether ornot processing of all of the important nodes is complete (Step S407).When it is determined that processing of all of the important nodes isnot complete, the processing returns to Step S400, and the estimationmodule 312 executes the same processing on a new important node. On theother hand, when it is determined that processing of all of theimportant nodes is complete, the estimation module 312 proceeds theprocessing to Step S408.

In the processing from Step S408 onwards, the estimation module 312generates a matrix representing the strength of the associations amongthe plurality of task nodes based on the weighting management table 327.In the following description, the matrix representing the strength ofthe associations among the plurality of task nodes is also referred toas an association matrix. In this embodiment, an edge weighting iscalculated for each adjustment parameter, and hence the estimationmodule 312 generates an association matrix for each adjustmentparameter.

The estimation module 312 selects the adjustment parameter to beprocessed (Step S408). Specifically, the estimation module 312 extractsall the types of adjustment parameters stored in the parameter type 1203of the weighting management table 327, and selects the adjustmentparameter to be processed from among the extracted adjustmentparameters.

The estimation module 312 generates the association matrix for theselected adjustment parameter (Step S409). Specifically, processing suchas the following is executed.

The estimation module 312 generates a matrix having n-rows andn-columns. In this case, “n” represents the number of task nodes, whichmatches the number of task nodes registered in the weighting managementtable 327. At this stage, all of the values of the matrix elements areset to “0”.

In this embodiment, the identifier of the task node corresponds to therow and column of the matrix. For example, “task node 1” corresponds torow 1, column 1. In this case, the matrix component at row 1, column 1,represents the strength of the association of the “task node 1” itself.The matrix component at row 1, column n, or row n, column 1, representsthe strength of the association between the “task node 1” and anothertask node.

The estimation module 312 refers to the weighting management table 327,and sets the value stored in the weighting 1205 of the entry having“node” in the type 1202 as the diagonal components of the matrix. Inthis embodiment, the diagonal components of the matrix components do notdepend on the adjustment parameter.

The estimation module 312 refers to the weighting management table 327,and retrieves an entry having “edge” stored in the type 1202 and theselected adjustment parameter stored in the parameter type 1203.Further, the estimation module 312 sets the value stored in theweighting 1205 as the off-diagonal components of the matrix based on thetask node identifier 1204 of the retrieved entry.

According to the above-mentioned processing, an association matrix suchas that shown in FIG. 13 can be generated from the weighting managementtable 327.

The above is a description of the processing carried out in Step S409.

Next, the estimation module 312 specifies the impact range in theoperations system based on the generated association matrix (Step S410).

Specifically, the estimation module 312 specifies a matrix component inwhich a value larger than a predetermined threshold is set. Theestimation module 312 can specify the impact range of the operationssystem caused by fluctuations in the load of the important nodes basedon an estimated matrix component. The estimation module 312 storesimpact range data in which the identifier of the important node, theadjustment parameter, and the specified matrix component are associatedwith one another in a work area of the memory 302.

When the management server 100 receives an instruction from the user todisplay the impact range, the management server 100 displays anoperations system such as that illustrated in FIG. 1 based on the nodemanagement table 323 and the association matrix. Further, based on theimpact range data, the management server 100 can display an impact rangesuch as that illustrated in FIG. 1.

It should be noted that the instruction from the user includes at leastany one of the identifier of the important node and the adjustmentparameter.

The above is a description of the processing carried out in Step S410.

Next, the estimation module 312 determines whether or not processing ofall of the adjustment parameters is complete (Step S411). When it isdetermined that processing of all of the adjustment parameters is notcomplete, the processing returns to Step S408, and the estimation module312 executes the same processing on a new adjustment parameter.

When it is determined that processing of all of the adjustmentparameters is complete, the estimation module 312 finishes theprocessing.

FIG. 20 is a flowchart for illustrating an example of the monitoringprocessing of the first embodiment. The operations system monitoringmodule 313 included in the control module 201 executes the monitoringprocessing.

The monitoring processing starts when the operations system monitoringmodule 313 detects a processing start trigger (Step S500). For example,when the monitoring processing is executed periodically, the operationssystem monitoring module 313 starts the monitoring processing whendetecting a lapse of a fixed period of time. Further, the operationssystem monitoring module 313 starts the monitoring processing when aninstruction is received from the user.

The operations system monitoring module 313 reads the system performancetable 326 and the impact range data, and monitors the state of the tasknodes included in the impact range in the operations system based on theread information (Step S501).

It should be noted that various methods may be employed as the method ofmonitoring the load. For example, the operations system monitoringmodule 313 may obtain the state of the task nodes each time apredetermined time interval elapses. Further, in this embodiment, thestate of the task nodes included in the impact range is monitored.However, only the state of the important nodes may be monitored. Inaddition, the monitoring may be carried out on only a specificparameter, or on all parameters.

The operations system monitoring module 313 displays the loads of thetask nodes included in the impact range as a meter representing a loadratio (Step S502). In other words, a critical level representing thepossibility of the computer resources allocated to the task nodes beinginsufficient is displayed as a meter. Specifically, processing such asthe following is executed.

The operations system monitoring module 313 refers to the systemperformance table 326, and retrieves an entry matching the identifier ofan important node included in the impact range data. Next, theoperations system monitoring module 313 obtains the performancecharacteristic function from the function 1106 of a row having anidentifier stored in the associated node identifier 1103 that matchesthe identifier of the task node for which a load has been obtained, andan adjustment parameter type 1105 that matches the parametercorresponding to the obtained load.

The operations system monitoring module 313 calculates the current loadratio with respect to the maximum load based on the performancecharacteristic function and the value of the obtained load of the tasknode. It should be noted that the maximum load can be calculated basedon the domain X and the performance characteristic function.

The operations system monitoring module 313 displays a meter 15 such asthat illustrated in FIG. 1 based on the calculated load ratio. The aboveis a description of the processing carried out in Step S502.

Next, the operations system monitoring module 313 determines whether ornot there is a task node having an increased load (Step S503).

Specifically, the operations system monitoring module 313 determineswhether or not there is a task node having a load ratio calculated inStep S502 that is larger than a predetermined threshold. For example,the operations system monitoring module 313 determines whether or notthere is a task node having a load ratio larger than 80%.

Further, the operations system monitoring module 313 may determine thatthere is a task node having an increased load when an instruction tochange the configuration of the task nodes has been received from theuser who has seen the display of the meter 15.

When it is determined that there are no task nodes having an increasedload, the processing returns to Step S501, and the operations systemmonitoring module 313 continues to monitor the state of the operationssystem.

When it is determined that there is a task node having an increasedload, the operations system monitoring module 313 refers to the rulemanagement table 328, and retrieves a rule that is applicable to thetask node (Step S504). Specifically, processing such as the following isexecuted.

The operations system monitoring module 313 refers to the nodemanagement table 323, and retrieves an entry having an identifier of thetask node identifier 801 that matches the identifier of the task nodespecified in Step S504. The operations system monitoring module 313obtains the values stored in the task type 804 and the task identifier805 of the retrieved entry.

The operations system monitoring module 313 refers to the rulemanagement table 328, and retrieves an entry having a task identifier1401 that matches the value obtained from the task identifier 805 and atask type 1403 that matches the value obtained from the task type 804.

The above is a description of the processing carried out in Step S504.It should be noted that when there are a plurality of task nodes havingan increased load, the operations system monitoring module 313 executesthe above-mentioned processing on each of those task nodes.

Next, the operations system monitoring module 313 displays the ruleretrieved from the rule management table 328 along with an alert (StepS505). Then, the processing returns to Step S501, and the operationssystem monitoring module 313 continues to monitor the operations system.

Specifically, the operations system monitoring module 313 displays tothe user the content stored in the rule 1406 and the numeral value setin the priority order 1405 of the entry retrieved from the rulemanagement table 328. As a result, the user can grasp the fact thatimprovement to the performance of the operations system is necessary,and grasp the change content for improving the performance of theoperations system, without needing to grasp the detailed state of theconfiguration of the physical layer and logical layer.

It should be noted that in this embodiment, the operations systemmonitoring module 313 only displays the rule. However, processing forchanging the configuration of the operations system may be executedbased on the rule. Further, the rule may be displayed along with costinformation, such as the cost when the rule is applied and a servicesuspension time.

It should be noted that this embodiment is described based on an exampleof a system having a three-layer configuration, namely, a physicallayer, a logical layer, and a task layer. However, this embodiment isnot limited to such a configuration. This embodiment may also be appliedin a system having a two-layer configuration consisting of a physicallayer and a task layer. In other words, this embodiment may be appliedeven in a system in which a physical node is assigned to every tasknode.

It should also be noted that the logical layer may have a multi-levelstructure. For example, the logical layer may have a two-level structurein which a plurality of logical computers are built by logicallypartitioning a blade server 223, and a plurality of virtual computersare made to run on each of the logical computers.

According to the first embodiment, the management server 100 canestimate the performance limit of the operations system by specifyingimportant task nodes based on configuration information on the physicallayer and logical layer, and executing a stress test on the importanttask nodes. An example of a method of applying the first embodiment isdescribed below.

The performance limit of a computer system for a specific operationssystem can be estimated by building the specific operations system onthe computer system, and executing node selection processing,measurement processing, and counting processing by the management server100.

As a result, the provider operating the computer system can estimate thecomputer resources required to build the specific operations system, andcan design a computer system specifically adapted to the specificoperations system.

Further, the execution of the monitoring processing by the managementserver 100 enables the management costs of the user operating theoperations system to be reduced.

Modified Example

In the node selection processing, the weighting calculation module 310only calculates the weighting of the selected task node. However, theweighting calculation module 310 may also calculate the weighting of anedge connecting the selected task node and another task node.

An example is described below of a case in which the logical nodeassigned to a selected task node (first task node) forms a cluster withanother logical node of the same physical server, and a task node(second task node) assigned to the another logical node exists.

In Step S201, the weighting calculation module 310 refers to thetopology management table 320 and the logical configuration managementtable 321, and determines whether or not there is a special connectionrelationship between the selected task node and the another task nodebased on the identifiers stored in the task node identifier 801 and theconnected node identifier 806 of the entry corresponding to the selectedtask node.

When it is determined that there is a special connection relationship,in Step S202, the weighting calculation module 310 calculates the eigenvalue stored in the system configuration 608 as the weighting of theedge connecting the first task node and the second task node.

In Step S203, the weighting calculation module 310 registers thecalculated edge weighting in the weighting management table 327.Specifically, processing such as the following is executed.

The weighting calculation module 310 adds an entry to the weightingmanagement table 327, and sets a predetermined identifier in theidentifier 1201 of the added entry. The weighting calculation module 310sets “edge” in the type 1202 of the added entry, and sets the identifierof the first task node and the identifier of the second task node in thetask node identifier 1204. It should be noted that the parameter type1203 remains blank.

Further, the weighting calculation module 310 sets a conversion formulathat uses the calculated edge weighting as a weighting coefficient inthe weighting 1205 of the added entry. A conversion formula such as theweighting 1205 of the entry having “102” in the identifier 1201 of FIG.12 is set. In this case, “0.8” is the weighting coefficient, and “m” isthe weighting of the edge determined based on the measurement result. Atthis point, the edge weighting is unknown, and hence the edge weightingis set as a variable. It should be noted that the conversion formula isnot limited to a conversion formula such as that shown in FIG. 12. Forexample, a conversion formula involving addition, division, and the likemay also be employed.

The remaining processing is the same as the processing in the firstembodiment, and hence a description thereof is omitted here. It shouldbe noted that the same processing may be applied on the physical nodes.

In the counting processing, in Step S404, the estimation module 312calculates a corrected edge weighting by substituting the calculatededge weighting into the conversion formula stored in the weighting 1205,and overwrites the weighting in the weighting 1205 with the calculatededge weighting.

Thus, with the use of a weighting coefficient, a measurement result thatbetter reflects the configuration of the physical layer and logicallayer can be obtained.

Second Embodiment

In the measurement processing of the first embodiment, the managementserver 100 measures the load of the task nodes for all of theverification methods. However, there are cases in which the adjustmentparameter is obvious in advance, or in which faster measurementprocessing is necessary. In a second embodiment of this invention, theuser designates the adjustment parameter in advance. The secondembodiment is described while focusing on the differences from the firstembodiment.

The system configuration, the configuration of the management server100, and the configuration of the blade servers 223 in the secondembodiment are the same as in the first embodiment, and hence adescription thereof is omitted here. Further, the content of themanagement table group 202 in the management server 100 in the secondembodiment is also the same as in the first embodiment, and hence adescription thereof is omitted here. In addition, the node selectionprocessing, the counting processing, and the monitoring processing inthe second embodiment are the same as in the first embodiment, and hencea description thereof is omitted here.

In the second embodiment, a part of the processing content of themeasurement processing is different. Specifically, the measurementmodule 311 displays to the user an orientation parameter registrationscreen 2100 for designating the verification method to be applied whenstarting the measurement processing. The orientation parameterregistration screen 2100 is now described.

FIG. 21 is an explanatory diagram for illustrating an example of theorientation parameter registration screen 2100 of the second embodiment.

A selection item display area 2110, a register button 2120, aregistration content display area 2130, a setting button 2140, and acancel button 2150 are displayed on the orientation parameterregistration screen 2100.

The selection item display area 2110 is an area for displayinginformation for designating the adjustment parameter. The selection itemdisplay area 2110 includes a task node selection item 2111 and aparameter orientation selection item 2112.

The task node selection item 2111 is an item for selecting an importantnode. The parameter orientation direction selection item 2112 is an itemfor designating the adjustment parameter to be applied on the importantnode. The measurement module 311 displays the important node in the tasknode selection item 2111 based on the verification list. The parameterorientation selection item 2112 displays an adjustment parametercondition, the type of the adjustment parameter, or the like, ratherthan displaying the adjustment parameter itself. For example,“processor-oriented” or the like is displayed as a display fordesignating the adjustment parameter associated with the processor.

In the example illustrated in FIG. 21, the task node selection item 2111and the parameter orientation selection item 2112 are displayed aspull-down menus.

The register button 2120 is an operation button for registering theoperation content of the selection item display area 2110. When the userpresses the register button 2120, the content set in the selection itemdisplay area 2110 is displayed in the registration content display area2130.

The registration content display area 2130 is an area for displaying thesetting content of the selection item display area 2110. Theregistration content display area 2130 includes a task node identifier2131 and a parameter orientation 2132.

The task node identifier 2131 is the identifier of the important node.The parameter orientation 2132 is the parameter orientation to beapplied on the important node.

The setting button 2140 is an operation button for reflecting thecontent displayed in the registration content display area 2130 in themeasurement processing. When the user presses the setting button 2140,the measurement module 311 starts the processing of Step S300. Thecancel button 2150 is an operation button for cancelling the contentdisplayed in the registration content display area 2130.

When the setting button 2140 is pressed, the measurement module 311temporarily stores the content displayed in the registration contentdisplay area 2130 in a work area of the memory 302. Further, themeasurement module 311 specifies the method of verifying the selectedimportant node based on the content set by using the orientationparameter registration screen 2100 (Step S301).

For example, when “processor-oriented” is designated for the selectedimportant node, the measurement module 311 retrieves only theverification methods having an adjustment parameter type 1004 that isassociated with the processor from among the verification methodsregistered in the adjustment method management table 325. The remainingprocessing is the same as in the first embodiment, and hence adescription thereof is omitted here.

According to the second embodiment, the management server 100 is capableof executing a stress test for only the necessary adjustment parameters.As a result, the load of the measurement processing can be reduced, andthe speed of the measurement processing can be increased.

Third Embodiment

In a third embodiment of this invention, in the node selectionprocessing, the management server 100 selects the important nodes byusing the configuration of the task layer in addition to using theconfiguration of the physical layer and logical layer. The thirdembodiment is described while focusing on the differences from the firstembodiment.

The system configuration, the configuration of the management server100, and the configuration of the blade servers 223 in the thirdembodiment are the same as in the first embodiment, and hence adescription thereof is omitted here. The content of the management tablegroup 202 in the management server 100 in the third embodiment is alsothe same as in the first embodiment, and hence a description thereof isomitted here. In addition, the measurement processing, countingprocessing, and monitoring processing are the same as in the firstembodiment, and hence a description thereof is omitted here. In thethird embodiment, a part of the node selection processing is differentfrom the first embodiment.

After selection of the task node (Step S200), the weighting calculationmodule 310 obtains the weighting associated with the selected task nodefrom the topology management table 320, the logical configurationmanagement table 321, and the task management table 322 (Step S201).

The method of obtaining the eigen values from the topology managementtable 320 and the logical configuration management table 321 is the sameas in the first embodiment, and hence a description thereof is omittedhere. In this case, a method in which the weighting calculation module310 obtains the eigen value of the task node from the task managementtable 322 is described. The weighting calculation module 310 refers tothe task management table 322, and retrieves an entry having a taskidentifier 701 that matches the task identifier 805 of the entryselected from the node management table 323. The weighting calculationmodule 310 obtains the value of the eigen value 706 of the retrievedentry.

In Step S202, the weighting calculation module 310 calculates theweighting of the task node by using the eigen values obtained from thetopology management table 320, the logical configuration managementtable 321, and the task management table 322. The remaining processingis the same as in the first embodiment, and hence a description thereofis omitted here.

According to the third embodiment, the selection of the important nodescan be performed while also considering information on the task layer,and hence a more accurate performance limit of the operations system canbe estimated. An example of a method of providing the third embodimentis described below.

The performance limit of the operations system with respect to a knowncomputer system can be estimated by building a new operations system ona known computer system, and executing node selection processing,measurement processing, and counting processing by the management server100.

As a result, the user operating the operations system can grasp thecomputer resources required for the operations system, the configurationto be adjusted, the setting items to be adjusted, and the like.

Further, the execution of the monitoring processing by the managementserver 100 enables the management costs of the user operating theoperations system to be reduced.

Fourth Embodiment

In a fourth embodiment of this invention, the management server 100reflects the configuration of the task layer in an association matrix inthe counting processing.

The system configuration, the configuration of the management server100, and the configuration of the blade servers 223 in the fourthembodiment are the same as in the first embodiment, and hence adescription thereof is omitted here. Further, the content of themanagement table group 202 in the management server 100 in the fourthembodiment is also the same as in the first embodiment, and hence adescription thereof is omitted here. The node selection processing,counting processing, and monitoring processing in the fourth embodimentare the same as in the first embodiment, and hence a description thereofis omitted here. In the fourth embodiment, a part of the countingprocessing is different from the first embodiment.

The processing from Step S400 to Step S407 is the same as in the firstembodiment.

After the estimation module 312 has selected the adjustment parameter tobe processed (Step S408), the estimation module 312 generates anassociation matrix (Step S409). Specifically, processing such as thefollowing is executed.

First, the estimation module 312 generates an association matrix basedon the weighting management table 327 in accordance with the sameprocedure as in the first embodiment. Next, the estimation module 312generates a superposition matrix based on configuration information onthe task layer. The estimation module 312 reflects the superpositionmatrix in the association matrix. For example, the estimation module 312multiplies, or, adds the superposition matrix to the association matrix.

In Step S410, the estimation module 312 specifies an impact range basedon the association matrix in which the superposition matrix isreflected.

In this case, an example of the processing for generating thesuperposition matrix is described with reference to FIG. 22. FIG. 22 isa flowchart for illustrating an example of the processing for generatingthe superposition matrix of the fourth embodiment.

The estimation module 312 generates a matrix having n-rows and n-columnsin which all of the matrix components are “0” (Step S600). Theestimation module 312 sets the eigen value of the task node for thediagonal components (Step S601).

Specifically, the estimation module 312 selects one entry from the nodemanagement table 323, and obtains the value of the task identifier 805of the selected entry. The estimation module 312 refers to the taskmanagement table 322, and retrieves an entry having a task identifier701 that matches the value obtained from the task identifier 805. Theestimation module 312 obtains the value of the eigen value 706 of theretrieved entry, and sets the value of the eigen value 706 for thediagonal components of the matrix corresponding to the selected tasknode.

The estimation module 312 executes the above-mentioned processing on allof the entries in the node management table 323, that is, on all of thetask nodes. As a result, the values for the diagonal components of thematrix are set.

Next, the estimation module 312 starts loop processing of the task nodes(Step S602). At this stage, the estimation module 312 selects one entryfrom the node management table 323.

The estimation module 312 specifies the associated nodes connected tothe task node corresponding to the selected entry (Step S603).Specifically, the estimation module 312 obtains the identifier of thetask node from the connected node identifier 806 of the selected entry.In the following description, the task node corresponding to theconnected node identifier 806 is also referred to as a connected node.

The estimation module 312 starts loop processing of the specifiedconnected nodes (Step S604). At this stage, the estimation module 312selects one identifier of the connected node to be processed from amongthe obtained identifiers of the connected nodes.

The estimation module 312 confirms the configuration associated with thetask node and connected node (Step S605), and determines whether or notthere is a special connection relationship between the task node and theconnected node (Step S606). Specifically, processing such as thefollowing is executed.

First, the estimation module 312 confirms the configuration of the tasknode. The estimation module 312 refers to the entry corresponding to thetask node in the node management table 323, obtains the value of theassigned node identifier 803 and the value of the task identifier 805,and obtains information on the associated information 807.

The estimation module 312 refers to the task management table 322, andretrieves the entry having a value of the task identifier 701 thatmatches the value obtained from the task identifier 805. The estimationmodule 312 obtains the eigen value 706 of the retrieved entry.

The estimation module 312 specifies the node assigned to the task nodebased on the value obtained from the assigned node identifier 803.

When a physical node is assigned for the task node, the estimationmodule 312 refers to the topology management table 320, and retrievesthe entry having a value of the physical node identifier 503 thatmatches the value of the assigned node identifier. The estimation module312 obtains information from the reliability type 507 of the retrievedentry, and obtains the value from the eigen value 508.

When a logical node is assigned for the task node, the estimation module312 refers to the logical configuration management table 321, andretrieves the entry having a value of the logical node identifier 603that matches the value of the assigned node identifier. The estimationmodule 312 obtains information from the system configuration 608 of theretrieved entry, and obtains the value from the eigen value 610.Further, based on the value of the physical node identifier 607 of theretrieved entry, the estimation module 312 refers to the topologymanagement table 320, obtains information from the reliability type 507of the entry corresponding to the physical node, and obtains the valuefrom the eigen value 508.

The same processing is also carried out on the configuration of theconnected node.

The estimation module 312 analyzes the configuration of the task nodeand connected node, and determines whether or not there is a specialconnection relationship. For example, when the task node and theconnected node are each logical nodes created on the same physicalserver and forming a cluster, the estimation module 312 determines thatthere is a special connection relationship.

The above is a description of the processing carried out in Step S605and Step S606.

When it is determined that there is not a special connectionrelationship between the task node and the connected node, theestimation module 312 proceeds the processing to Step S609.

When it is determined that there is a special connection relationshipbetween the task node and the connected node, the estimation module 312calculates the eigen value of the edge by using the eigen value obtainedin Step S605 (Step S607). Various methods of calculating the eigen valueof the edge may be employed. For example, the eigen value of the edgemay be calculated by adding together the value of the eigen value 610associated with the task node and the value of the eigen value 610associated with the connected node. It should be noted that in thisembodiment, the method of calculating the eigen value of the edge is notlimited.

The estimation module 312 sets the calculated eigen value of the edgefor the diagonal components corresponding to the edge (Step S608).

The estimation module 312 determines whether or not the processing ofall of the connected nodes specified in Step S603 is complete (StepS609). When it is determined that the processing of all of the connectednodes specified in Step S603 is not complete, the processing returns toStep S604, and the estimation module 312 executes the same processing ona new connected node.

When it is determined that the processing of all of the connected nodesspecified in Step S603 is complete, the estimation module 312 determineswhether or not the processing of all of the task nodes is complete (StepS610).

When it is determined that the processing of all of the task nodes isnot complete, the processing returns to Step S602, and the estimationmodule 312 executes the same processing on a new task node. On the otherhand, when it is determined that the processing of all of the task nodesis complete, the estimation module 312 finishes the processing.

The above is a description of the generation processing of thesuperposition matrix.

According to the fourth embodiment, the performance limit of theoperations system can be estimated in consideration of information onthe physical layer, logical layer, and task layer. It should be notedthat the fourth embodiment may be provided using the same method as inthe third embodiment.

Fifth Embodiment

In a fifth embodiment of this invention, the management server 100incorporates impact range data into template information to be used whenbuilding the operations system.

In the related art, the configuration of the operations system ismanaged as template information. The template information is, forexample, data in an extensible markup language (XML) format. Themanagement server 100 incorporates the result of counting processinginto template information corresponding to an operations system that hascarried out a stress test.

The system configuration and the configuration of the blade servers 223in the fifth embodiment are the same as in the first embodiment, andhence a description thereof is omitted here. A part of the configurationof the management server 100 of the fifth embodiment is different.

FIG. 23 is a block diagram for illustrating a configuration example ofthe management server 100 of the fifth embodiment.

The hardware configuration of the management server 100 is the same asin the first embodiment, and hence a description thereof is omittedhere. In the fifth embodiment, the control module 201 includes a metatag insertion module 2301. Further, the management table group 202 ofthe fifth embodiment includes template management information 2302.

The template management information 2302 stores information on theconfiguration of the operations system. Specifically, the templatemanagement information 2302 includes a plurality of entries in whichidentification information on the operations system and templateinformation representing the configuration of the operations system areassociated with each other. In this embodiment, the template informationis data in the XML format.

The meta tag insertion module 2301 is configured to generate a meta tag(meta information) based on the result of the counting processing, andinsert the generated meta tag into a meta tag of the templateinformation. Details of the processing executed by the meta taginsertion module 2301 are described later with reference to FIG. 24.

The remaining configuration is the same as in the first embodiment, andhence a description thereof is omitted here. Further, the node selectionprocessing, measurement processing, counting processing, and monitoringprocessing of the fifth embodiment are the same as in the firstembodiment, and hence a description thereof is omitted here.

FIG. 24 is a flowchart for illustrating an example of the meta taggeneration processing of the fifth embodiment.

The meta tag insertion module 2301 of the control module 201 starts themeta tag generation processing after the counting processing hasfinished.

First, the meta tag insertion module 2301 starts loop processing of theedges (Step S700). At this stage, the meta tag insertion module 2301refers to the weighting management table 327, and selects one entryhaving “edge” stored in the type 1202.

The meta tag insertion module 2301 specifies two task nodes that areconnected via the edge corresponding to the selected entry (Step S701).

Specifically, the meta tag insertion module 2301 refers to the task nodeidentifier 1204 of the selected entry, and specifies the two task nodesconnected via the edge corresponding to the selected entry.

The meta tag insertion module 2301 obtains the template informationcorresponding to the operations system that carried out the stress testfrom the template management information 2302 (Step S702). The meta taginsertion module 2301 generates a meta tag relating to the edge and ameta tag relating to the two task nodes connected via the edge (StepS703). For example, meta tags such as the following are generated.

The meta tag insertion module 2301 generates a meta tag includinginformation on the edge and a meta tag including information on the twotask nodes.

Further, the meta tag insertion module 2301 refers to the systemperformance table 326, and generates a meta tag representing the valueof the performance limit of the two task nodes. The meta tag insertionmodule 2301 retrieves the row having an important node identifier 1102and an associated node identifier 1103 that match the identifiers of thetwo task nodes, and obtains the function stored in the function 1106 ofthat row. In addition, based on the function, the meta tag insertionmodule 2301 calculates a value X that saturates the response time of thesystem, and generates a meta tag including the percentile of that value.

The meta tag insertion module 2301 inserts the generated meta tag intothe obtained template information (Step S704). The meta tag insertionmodule 2301 determines whether or not processing on all of the edges iscomplete (Step S705).

When it is determined that processing on all of the edges is notcomplete, the processing returns to Step S700, and the meta taginsertion module 2301 executes the same processing on a new edge. Whenit is determined that processing on all of the edges is complete, themeta tag insertion module 2301 finishes the processing.

According to the fifth embodiment, the measurement result of the stresstest of the operations system can be inserted into template informationto be used when generating the operations system. As a result, aperformance characteristic of the operations system can be grasped atthe time of building the operations system, and, the systemconfiguration and the like required to build the operations system canbe grasped.

For example, when creating a virtual computer during building of theoperations system, a system that gives consideration to a performancecharacteristic such as open virtualization format (OVF) can be built.Therefore, the portability and migratability of the system can beimproved.

Meta tags include a method of responding to any of the physical nodes,logical nodes, and task nodes, and hence when a performance failure hasoccurred in the computer system or operations system, the failure can behandled in a rapid and correct manner based on the meta tag.

This invention is not limited to the above-described embodiments butincludes various modifications. The above-described embodiments areexplained in details for better understanding of this invention and arenot limited to those including all the configurations described above. Apart of the configuration of one embodiment may be replaced with that ofanother embodiment; the configuration of one embodiment may beincorporated to the configuration of another embodiment. A part of theconfiguration of each embodiment may be added, deleted, or replaced bythat of a different configuration.

The above-described configurations, functions, processing modules, andprocessing means, for all or a part of them, may be implemented byhardware: for example, by designing an integrated circuit. Theabove-described configurations and functions may be implemented bysoftware, which means that a processor interprets and executes programsproviding the functions.

The information of programs, tables, and files to implement thefunctions may be stored in a storage device such as a memory, a harddisk drive, or an SSD (a Solid State Drive), or a storage medium such asan IC card, or an SD card.

The drawings shows control lines and information lines as considerednecessary for explanation but do not show all control lines orinformation lines in the products. It can be considered that almost ofall components are actually interconnected.

What is claimed is:
 1. A system management method for a managementcomputer coupled to a computer system, the management computerincluding: a first processor; a first memory coupled to the firstprocessor; and a first interface coupled to the first processor, thecomputer system including a plurality of computers, each of theplurality of computers including: a second processor; a second memorycoupled to the second processor; and a second interface coupled to thesecond processor, an operations system being built thereon the computersystem, the operations system including a plurality of task nodes eachhaving allocated thereto one of computer resources of one computer amongthe plurality of computers and computer resources of a virtual computergenerated on at least one computer among the plurality of computers, thesystem management method including: a first step of analyzing, by themanagement computer, a configuration of the computer system forspecifying at least one important node, which is an important task nodein the operations system; a second step of changing, by the managementcomputer, an allocation amount of the computer resources allocated tothe at least one important node for measuring a load of the operationssystem; a third step of calculating, by the management computer, a firstweighting representing a strength of associations among the plurality oftask nodes based on a measurement result of the load; and a fourth stepof specifying, by the management computer, a range impacted by a changein the load of the at least one important node based on the calculatedfirst weighting; wherein the management computer stores: topologymanagement information for managing an apparatus configuration of eachof the plurality of computers and a coupling configuration of theplurality of computers; logical configuration management information formanaging an apparatus configuration of a plurality of virtual computersand a coupling configuration of the plurality of virtual computers; andnode management information for managing a connection configuration ofthe plurality of task nodes, and, a correspondence relationship of theone computer and the virtual computer allocating the computer resourcesto each of the plurality of task nodes, wherein the topology managementinformation includes a first eigen value representing an importancelevel of a physical configuration in the computer system, the firsteigen value being set based on the apparatus configuration of each ofthe plurality of computers and the coupling configuration of each of theplurality of computers, wherein the logical configuration managementinformation includes a second eigen value representing an importancelevel of a logical configuration in the computer system, the secondeigen value being set based on the apparatus configuration of theplurality of virtual computers and the coupling relationship of each ofthe plurality of virtual computers, and wherein the first step includes:referring to the node management information for specifying, for each ofthe plurality of task nodes, the one computer and the virtual computerallocating the computer resources to the each of the plurality of tasknodes; referring, for a task node to be allocated with the computerresources of the one computer, to the topology management informationfor obtaining the at least one first eigen value associated with the onecomputer allocating the computer resources to the task node, andcalculating a second weighting representing an importance level of thetask node to be allocated with the computer resources of the onecomputer based on the at least one first eigen value; referring, for atask node to be allocated with the computer resources of the virtualcomputer, to the logical configuration management information forobtaining the at least one second eigen value associated with thevirtual computer allocating the computer resources to the task node,referring to the logical configuration management information and thetopology management information for obtaining the at least one firsteigen value associated with a computer generated by the virtual computerallocating the computer resources to the task node, and calculating thesecond weighting based on the at least one first eigen value and the atleast one second eigen value; specifying at least one of the importantnodes from among the plurality of task nodes based on the secondweighting of each of the plurality of task nodes; wherein the secondstep includes: selecting one important node to be processed from amongthe at least one important node; and changing the allocation amount ofthe computer resources to be allocated to the important node to beprocessed for measuring the load of each of the plurality of task nodes.2. The system management method according to claim 1, wherein the secondstep includes: calculating, based on a measurement result of the load, aperformance characteristic function representing an association betweenthe allocation amount of the computer resources to be allocated to theimportant node to be processed and the load of each of the plurality oftask nodes, and wherein the third step includes generating anassociation matrix including the first weighting as an off-diagonalcomponent value and the second weighting as a diagonal component value.3. The system management method according to claim 2, wherein each ofthe plurality of task nodes is configured to execute a predeterminedtask, wherein the management computer stores task management informationin which a type of task to be executed by the operations system, aconfiguration required for the task, and a fourth eigen valuerepresenting an importance level of the task are associated with oneanother, and wherein the fourth step includes: referring to the nodemanagement information for selecting one task node to be processed;referring to the node management information for specifying a connectednode, which is a task node connected to the task node to be processed;obtaining, based on the node management information and the taskmanagement information, the fourth eigen value corresponding to the typeof the task to be executed by the task node to be processed and thefourth eigen value corresponding to the type of the task to be executedby the connected node; calculating a second weighting representing astrength of an association between the task node to be processed and theconnected node by using the fourth eigen value of the task node to beprocessed and the fourth eigen value of the connected node; generating asuperposition matrix including the second weighting as an off-diagonalcomponent value and a eigen value of the task node as a diagonalcomponent value; and correcting the association matrix by using thesuperposition matrix.
 4. The system management method according to claim2, wherein each of the plurality of task nodes is configured to executea predetermined task, wherein the management computer stores rulemanagement information in which a type of the task and a method ofchanging the computer resources to be allocated to the task nodeexecuting the task are associated with one another, and wherein thesystem management method further includes: monitoring, by the managementcomputer, a load of each of the plurality of task nodes included in theimpacted range; estimating, by the management computer, a critical valuerepresenting a possibility of the computer resources of each of theplurality of task nodes being insufficient by using a value of the loadof each of the plurality of task nodes and the performancecharacteristic function; determining, by the management computer, foreach of the plurality of task nodes, whether or not there is a firsttask node having an estimated critical value equal to or more than afirst threshold; referring, by the management computer, to the rulemanagement information for specifying a method of changing the computerresources corresponding to the type of the task to be executed by thefirst task node in a case where there is the first task node having anestimated critical value equal to or more than the first threshold; andgenerating, by the management computer, information for displaying thespecified method of changing the computer resources.
 5. The systemmanagement method according to claim 2, wherein the management computerstores template information storing information required to build theoperations system, and wherein the system management method furtherincludes: specifying, by the management computer, a plurality of tasknodes that are associated with each other based on the first weighting;generating, by the management computer, meta information including aconnecting relationship of the specified plurality of task nodes and ameasurement result of the load; and adding, by the management computer,the meta information to the template information.
 6. A managementcomputer coupled to a computer system including a plurality ofcomputers, the management computer comprising: a first processor; afirst memory coupled to the first processor; and a first interfacecoupled to the first processor, each of the plurality of computersincluding: a second processor; a second memory coupled to the secondprocessor; and a second interface coupled to the second processor, anoperations system being built thereon the computer system, theoperations system including a plurality of task nodes each havingallocated thereto one of computer resources of one computer among theplurality of computers and computer resources of a virtual computergenerated on at least one computer among the plurality of computers, themanagement computer comprising: a weighting calculation moduleconfigured to analyze a configuration of the computer system, to therebycalculate a first weighting representing an importance level of each ofthe plurality of task nodes, and to specify at least one important node,which is an important task node in the operations system, based on thefirst weighting; a measurement module configured to change an allocationamount of the computer resources allocated to the at least one importantnode and measure a load of the operations system; and an estimationmodule configured to calculate a second weighting representing astrength of associations among the plurality of task nodes based on ameasurement result of the load, and specify a range impacted by a changein the load of the at least one important node based on the calculatedsecond weighting; wherein the management computer stores: topologymanagement information for managing an apparatus configuration of eachof the plurality of computers and a coupling configuration of theplurality of computers; logical configuration management information formanaging an apparatus configuration of a plurality of virtual computersand a coupling configuration of the plurality of virtual computers; andnode management information for managing a connection configuration ofthe plurality of task nodes, and, a correspondence relationship of theone computer and the virtual computer allocating the computer resourcesto each of the plurality of task nodes, wherein the topology managementinformation includes a first eigen value representing an importancelevel of a physical configuration in the computer system, the firsteigen value being set based on the apparatus configuration of each ofthe plurality of computers and the coupling configuration of each of theplurality of computers, wherein the logical configuration managementinformation includes a second eigen value representing an importancelevel of a logical configuration in the computer system, the secondeigen value being set based on the apparatus configuration of theplurality of virtual computers and the coupling relationship of each ofthe plurality of virtual computers, and wherein the weightingcalculation module is configured to: refer to the node managementinformation for specifying, for each of the plurality of task nodes, theone computer and the virtual computer allocating the computer resourcesto the each of the plurality of task nodes; refer, for a task node to beallocated with the computer resources of the one computer, to thetopology management information for obtaining the at least one firsteigen value associated with the one computer allocating the computerresources to the task node, and calculate the first weighting based onthe at least one first eigen value; refer, for a task node to beallocated with the computer resources of the virtual computer, to thelogical configuration management information for obtaining the at leastone second eigen value associated with the virtual computer allocatingthe computer resources to the task node, refer to the logicalconfiguration management information and the topology managementinformation for obtaining the at least one first eigen value associatedwith a computer generated by the virtual computer allocating thecomputer resources to the task node, and calculate the first weightingbased on the at least one first eigen value and the at least one secondeigen value; specify at least one of the important nodes from among theplurality of task nodes based on the second weighting of each of theplurality of task nodes; wherein the measurement module is configuredto: select one important node to be processed from among the at leastone important node; and change the allocation amount of the computerresources to be allocated to the important node to be processed formeasuring the load of each of the plurality of task nodes.
 7. Themanagement computer according to claim 6, wherein the measurement moduleis configured to: calculate, based on a measurement result of the load,a performance characteristic function representing an associationbetween the allocation amount of the computer resources to be allocatedto the important node to be processed and the load of each of theplurality of task nodes, and wherein the estimation module is configuredto generate an association matrix including the second weighting as anoff-diagonal component value and the first weighting as a diagonalcomponent value.
 8. The management computer according to claim 7,wherein each of the plurality of task nodes is configured to execute apredetermined task, wherein the management computer stores taskmanagement information in which a type of task to be executed by theoperations system, a configuration required for the task, and a fourtheigen value representing an importance level of the task are associatedwith one another, and wherein the estimation module is configured to:refer to the node management information for selecting one task node tobe processed; refer to the node management information for specifying aconnected node, which is a task node connected to the task node to beprocessed; obtain, based on the node management information and the taskmanagement information, the fourth eigen value corresponding to the typeof the task to be executed by the task node to be processed and thefourth eigen value corresponding to the type of the task to be executedby the connected node; calculate a second weighting representing astrength of an association between the task node to be processed and theconnected node by using the fourth eigen value of the task node to beprocessed and the fourth eigen value of the connected node; generate asuperposition matrix including the second weighting as an off-diagonalcomponent value and a eigen value of the task node as a diagonalcomponent value; and correct the association matrix by using thesuperposition matrix.
 9. The management computer according to claim 7,wherein each of the plurality of task nodes is configured to execute apredetermined task, wherein the management computer stores rulemanagement information in which a type of the task and a method ofchanging the computer resources to be allocated to the task nodeexecuting the task are associated with one another, wherein themanagement computer further comprises an operations system monitoringmodule configured to monitor the operations system based on themeasurement result of the load, and wherein the operations systemmonitoring module is configured to: monitor a load of each of theplurality of task nodes included in the impacted range; estimate acritical value representing a possibility of the computer resources ofeach of the plurality of task nodes being insufficient by using a valueof the load of each of the plurality of task nodes and the performancecharacteristic function; determine, for each of the plurality of tasknodes, whether or not there is a first task node having an estimatedcritical value equal to or more than a first threshold; refer to therule management information for specifying a method of changing thecomputer resources corresponding to the type of the task to be executedby the first task node in a case where there is the first task nodehaving an estimated critical value equal to or more than the firstthreshold; and generate information for displaying the specified methodof changing the computer resources.
 10. The management computeraccording to claim 7, wherein the management computer stores templateinformation storing information required to build the operations system,wherein the management computer further comprises a meta informationinsertion module configured to generate meta information including themeasurement result of the load, and wherein the meta informationinsertion module is configured to: specify a plurality of task nodesthat are associated with each other based on the second weighting;generate meta information including a connection relationship of thespecified plurality of task nodes and the measurement result of theload; and add the meta information to the template information.
 11. Anon-transitory computer-readable storage medium having stored thereon aprogram to be executed by a management computer coupled to a computersystem, the management computer including: a first processor; a firstmemory coupled to the first processor; and a first interface coupled tothe first processor, the computer system including a plurality ofcomputers, each of the plurality of computers including: a secondprocessor; a second memory coupled to the second processor; and a secondinterface coupled to the second processor, an operations system beingbuilt thereon the computer system, the operations system including aplurality of task nodes each having allocated thereto one of computerresources of one computer among the plurality of computers and computerresources of a virtual computer generated on at least one computer amongthe plurality of computers, the non-transitory computer-readable storagemedium having stored thereon a program for controlling the managementcomputer to execute: a first procedure of analyzing a configuration ofthe computer system for specifying at least one important node, which isan important task node in the operations system; a second procedure ofchanging an allocation amount of the computer resources allocated to theat least one important node for measuring a load of the operationssystem; a third procedure of calculating a first weighting representinga strength of associations among the plurality of task nodes based on ameasurement result of the load; and a fourth procedure of specifying arange impacted by a change in the load of the at least one importantnode based on the calculated first weighting; wherein the managementcomputer stores: topology management information for managing anapparatus configuration of each of the plurality of computers and acoupling configuration of the plurality of computers; logicalconfiguration management information for managing an apparatusconfiguration of a plurality of virtual computers and a couplingconfiguration of the plurality of virtual computers; and node managementinformation for managing a connection configuration of the plurality oftask nodes, and, a correspondence relationship of the one computer andthe virtual computer allocating the computer resources to each of theplurality of task nodes, wherein the topology management informationincludes a first eigen value representing an importance level of aphysical configuration in the computer system, the first eigen valuebeing set based on the apparatus configuration of each of the pluralityof computers and the coupling configuration of each of the plurality ofcomputers, wherein the logical configuration management informationincludes a second eigen value representing an importance level of alogical configuration in the computer system, the second eigen valuebeing set based on the apparatus configuration of the plurality ofvirtual computers and the coupling relationship of each of the pluralityof virtual computers, and wherein the first procedure includes theprocedures of: referring to the node management information forspecifying, for each of the plurality of task nodes, the one computerand the virtual computer allocating the computer resources to the eachof the plurality of task nodes; referring, for a task node to beallocated with the computer resources of the one computer, to thetopology management information for obtaining the at least one firsteigen value associated with the one computer allocating the computerresources to the task node and, calculating a second weightingrepresenting an importance level of the task node to be allocated withthe computer resources of the one computer based on the at least onefirst eigen value; referring, for a task node to be allocated with thecomputer resources of the virtual computer, to the logical configurationmanagement information for obtaining the at least one second eigen valueassociated with the virtual computer allocating the computer resourcesto the task node, referring to the logical configuration managementinformation and the topology management information for obtaining the atleast one first eigen value associated with a computer generated by thevirtual computer allocating the computer resources to the task node, andcalculating the second weighting based on the at least one first eigenvalue and the at least one second eigen value; specifying at least oneof the important nodes from among the plurality of task nodes based onthe second weighting of each of the plurality of task nodes; wherein thesecond procedure includes the procedures of: selecting one importantnode to be processed from among the at least one important node; andchanging the allocation amount of the computer resources to be allocatedto the important node to be processed for measuring the load of each theplurality of task nodes.
 12. The non-transitory computer-readablestorage medium according to claim 11, wherein the second procedureincludes the procedures of: calculating, based on a measurement resultof the load, a performance characteristic function representing anassociation between the allocation amount of the computer resources tobe allocated to the important node to be processed and the load of eachof the plurality of task nodes, and wherein the third procedure includesa procedure of generating an association matrix including the firstweighting as an off-diagonal component value and the second weighting asa diagonal component value.